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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Translator - Describe capabilities of Azure Language |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision |
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NEW QUESTION # 190
You need to predict the population size of a specific species of animal in an area.
Which Azure Machine Learning type should you use?
Answer: B
Explanation:
In Azure Machine Learning, regression is a supervised machine learning technique used to predict continuous numerical values based on input data. According to the Microsoft AI Fundamentals (AI-900) study guide and the Microsoft Learn module "Identify common types of machine learning," regression models are ideal when the goal is to estimate a quantity - such as price, temperature, or, in this case, population size.
In the scenario, the task is to predict the population size of a specific species within a defined area. Population size is a numerical, continuous value that varies depending on multiple factors (like time, environment, and resources). A regression algorithm, such as linear regression or decision tree regression, can be trained on historical data (e.g., species count, area, temperature, food availability) to forecast future population numbers.
Option analysis:
* A. Clustering: Used for unsupervised learning, where the goal is to group similar data points into clusters without predefined labels (e.g., grouping animals by behavior or habitat).
* C. Classification: Used to predict discrete categories or labels (e.g., "endangered" vs. "not endangered"), not numerical values.
Therefore, the correct machine learning type for predicting a continuous value such as population size is Regression.
NEW QUESTION # 191
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
In the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning (AML) learning paths, deploying a real-time inference pipeline refers to making a trained machine learning model available as a web service that can process incoming data and return predictions instantly. To achieve this, the model must be deployed to an infrastructure capable of handling continuous, low-latency requests with high reliability and scalability.
Microsoft's official guidance from Azure Machine Learning documentation specifies that:
* For testing or development, you can deploy to Azure Container Instances (ACI) because it provides a lightweight, temporary environment suitable for small-scale or non-production workloads.
* For production-grade, real-time inference, the deployment should be made to Azure Kubernetes Service (AKS).
AKS provides enterprise-level scalability, load balancing, and high availability, which are critical for serving real-time predictions to multiple consumers simultaneously. It manages containerized applications using Kubernetes orchestration, allowing the model to scale automatically based on traffic demands.
Azure Machine Learning Compute is mainly used for model training and batch inference pipelines, not real- time endpoints. A local web service is typically used only for debugging or offline testing on a developer machine and cannot be shared for external consumption.
Therefore, when deploying a real-time inference pipeline as a service for others to consume, the correct and Microsoft-verified option is Azure Kubernetes Service (AKS). This environment ensures production readiness, secure endpoint management, and scalability for live AI applications, fully aligning with best practices outlined in the Azure Machine Learning designer documentation and AI-900 exam objectives.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy
NEW QUESTION # 192
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
Answer: B
Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introd
NEW QUESTION # 193
correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 194
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 195
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